Metadata-Version: 2.4
Name: svv
Version: 0.0.47
Summary: svVascularize (svv): A synthetic vascular generation, modeling, and simulation package
Author: Zachary Sexton
Author-email: zsexton@stanford.edu
License: MIT
Keywords: modeling,simulation,tissue-engineering,3d-printing,fluid-dynamics
Classifier: Intended Audience :: Science/Research
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Programming Language :: Python :: 3.13
Classifier: Topic :: Scientific/Engineering
Classifier: Operating System :: Microsoft :: Windows
Classifier: Operating System :: POSIX :: Linux
Classifier: Operating System :: POSIX
Classifier: Operating System :: Unix
Classifier: Operating System :: MacOS
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Provides-Extra: accel
Requires-Dist: svv-accelerated==0.0.47; extra == "accel"
Provides-Extra: accelerated
Requires-Dist: svv-accelerated==0.0.47; extra == "accelerated"
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# svVascularize


[![Version](https://img.shields.io/pypi/v/svv.svg?logo=pypi&label=PyPI%20version)](https:://pypi.org/project/svv/)
![Platform](https://img.shields.io/badge/platform-macOS%20|%20linux%20|%20windows-blue)
![Latest Release](https://img.shields.io/github/v/release/SimVascular/svVascularize?label=latest)
[![codecov](https://codecov.io/github/SimVascular/svVascularize/graph/badge.svg)](https://codecov.io/github/SimVascular/svVascularize)
[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.15151168.svg)](https://doi.org/10.5281/zenodo.15151168)
[![Docs](https://img.shields.io/badge/docs-gh--pages-brightgreen)](https://simvascular.github.io/svVascularize/)
[![Telemetry](https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/zasexton/svVascularize/main/docs/badges/telemetry.json)](https://sentry.io)
<!-- smoke-test-badge -->
[![SVV passing](https://img.shields.io/static/v1?label=svv%20passing&message=linux%20ok%20%7C%20macos%20ok%20%7C%20windows%20ok&color=brightgreen)](https://github.com/zasexton/svVascularize/actions/workflows/basic-smoke-test.yml?query=branch%3Amain)
<!-- /smoke-test-badge -->

<p align="left">
The svVascularize (svv) is an open-source API for automated vascular generation and multi-fidelity hemodynamic simulation
written in Python. Often small-caliber vessels are difficult or infeasible to obtain from experimental data sources 
despite playing important roles in blood flow regulation and cell microenvironments. svVascularize aims to provide tissue 
engineers and computational hemodynamic scientists with de novo vasculature that can easily be applied in 
biomanufacturing applications or computational fluid dynamic (CFD) analysis.
</p>

* **Website:** https://simvascular.github.io/svVascularize/
* **PyPi:** https://pypi.org/project/svv/
* **Source code:** https://github.com/SimVascular/svVascularize

## Installation

The package is published on PyPI as `svv`:

```bash
pip install svv
```

On clusters / HPC systems (for example Stanford Sherlock), use a recent Python (3.9–3.12) and `pip`, and install into a 
clean virtual environment or user site-packages. 
